动⼿学深度学习A.Zhang,M.Li,Z.C.Lipton,andA.J.Smola2018年12⽉23⽇⽬录1引⾔11.1前⾔..........................................11.2深度学习简介.....................................61.3如何使⽤本书.....................................162预备知识212.1获取和运⾏本书代码.................................212.2数据操作.......................................252.3⾃动求梯度......................................332.4查阅MXNet⽂档...................................363深度学习基础413.1线性回归.......................................413.2线性回归的从零开始实现...............................473.3线性回归的Gluon实现................................533.4Softmax回归.....................................573.5图像分类数据集（Fashion-MNIST）........................623.6Softmax回归的从零开始实现............................663.7Softmax回归的Gluon实现.............................713.8多层感知机......................................73i3.9多层感知机的从零开始实现.............................813.10多层感知机的Gluon实现..............................833.11模型选择、⽋拟合和过拟合.............................843.12权重衰减.......................................933.13丢弃法.........................................1003.14正向传播、反向传播和计算图............................1053.15数值稳定性和模型初始化...............................1093.16实战Kaggle⽐赛：房价预测.............................1114深度学习计算1214.1模型构造.......................................1214.2模型参数的访问、初始化和共享...........................1264.3模型参数的延后初始化................................1324.4⾃定义层.......................................1344.5读取和存储......................................1374.6GPU计算.......................................1405卷积神经⽹络1455.1⼆维卷积层......................................1455.2填充和步幅......................................1515.3多输⼊通道和多输出通道...............................1555.4池化层.........................................1595.5卷积神经⽹络（LeNet）.............
